A deep learning-based method for detecting and identifying surface defects in polyimide foam

Currently, the detection and identification of surface defects in polyimide foam products mainly rely on on-site work experience, which has issues such as low detection accuracy, strong subjectivity, and low efficiency. Existing research on foam product defect detection primarily targets internal defects, lacking studies on the detection, identification, and classification of surface defects. Therefore, this article proposes a method for identifying and classifying surface defects in polyimide foam based on an improved GoogLeNet, aiming to quickly and accurately detect and identify surface defects in foam products. By optimizing the Inception blocks, introducing the ECA attention mechanism, and adding an LSTM network module, the model's recognition accuracy and generalization ability are effectively improved. In experiments, the model proposed in this article performed excellently on the foam surface defect dataset, showing a significant advantage in detection accuracy compared to other convolutional neural network models. The detection accuracy for pits and cracks reached 98.24% and 98.25%, respectively, providing a reliable reference for the detection of surface defects in industrial foam production.